Non-Destructive Evaluation of Asphalt Concrete Pavement System Using Fwd Tests Considering Modeling Errors

Abstract

The structural integrity assessment technique for the asphalt pavement system is studied considering the modeling errors introduced by material uncertainties. To this end, the artificial neural network is utilized to estimate the elastic modulus of soil layers using the measured deflection data from the FWD (Falling Weight Deflectometer) test. A wave analysis program for a multi-layered pavement system is developed based on the spectral element method for more accurate and faster calculation. The developed program is applied for the numerical simulation of the FWD tests, the generation of training and testing patterns for the neural network. The evaluation capability of neural network is investigated when the input data is corrupted by the modeling errors, and it is found that the estimation results can be significantly deviated due to the modeling errors. To reduce the effect of the modeling error, (in other words to improve the robustness of the algorithm), we proposed an alternative scheme to generate the training patterns considering modeling errors, and it is finally concluded that the estimation results can be improved by using the proposed training patterns from extensive numerical simulation study.